# Differential expression on Kallisto data
# Preliminary samples - 2016 dataset
# Packages and dependence
packageCheckClassic <- function(x){
#
for( i in x ){
if( ! require( i , character.only = TRUE ) ){
install.packages( i , dependencies = TRUE )
require( i , character.only = TRUE )
}
}
}
packageCheckClassic(c('DESeq2','adegenet','vsn','devtools','BiocManager','ggplot2','ggrepel','markdown','pheatmap','RColorBrewer','genefilter','gplots','vegan','dplyr','limma'))
## Le chargement a nécessité le package : DESeq2
## Le chargement a nécessité le package : S4Vectors
## Warning: le package 'S4Vectors' a été compilé avec la version R 4.1.3
## Le chargement a nécessité le package : stats4
## Le chargement a nécessité le package : BiocGenerics
##
## Attachement du package : 'BiocGenerics'
## Les objets suivants sont masqués depuis 'package:stats':
##
## IQR, mad, sd, var, xtabs
## Les objets suivants sont masqués depuis 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min
##
## Attachement du package : 'S4Vectors'
## Les objets suivants sont masqués depuis 'package:base':
##
## expand.grid, I, unname
## Le chargement a nécessité le package : IRanges
## Le chargement a nécessité le package : GenomicRanges
## Le chargement a nécessité le package : GenomeInfoDb
## Le chargement a nécessité le package : SummarizedExperiment
## Le chargement a nécessité le package : MatrixGenerics
## Le chargement a nécessité le package : matrixStats
##
## Attachement du package : 'MatrixGenerics'
## Les objets suivants sont masqués depuis 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars
## Le chargement a nécessité le package : Biobase
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
##
## Attachement du package : 'Biobase'
## L'objet suivant est masqué depuis 'package:MatrixGenerics':
##
## rowMedians
## Les objets suivants sont masqués depuis 'package:matrixStats':
##
## anyMissing, rowMedians
## Le chargement a nécessité le package : adegenet
## Le chargement a nécessité le package : ade4
##
## Attachement du package : 'ade4'
## L'objet suivant est masqué depuis 'package:GenomicRanges':
##
## score
## L'objet suivant est masqué depuis 'package:BiocGenerics':
##
## score
##
## /// adegenet 2.1.10 is loaded ////////////
##
## > overview: '?adegenet'
## > tutorials/doc/questions: 'adegenetWeb()'
## > bug reports/feature requests: adegenetIssues()
## Le chargement a nécessité le package : vsn
## Le chargement a nécessité le package : devtools
## Le chargement a nécessité le package : usethis
## Le chargement a nécessité le package : BiocManager
## Bioconductor version '3.14' is out-of-date; the current release version '3.16'
## is available with R version '4.2'; see https://bioconductor.org/install
##
## Attachement du package : 'BiocManager'
## L'objet suivant est masqué depuis 'package:devtools':
##
## install
## Le chargement a nécessité le package : ggplot2
## Le chargement a nécessité le package : ggrepel
## Le chargement a nécessité le package : markdown
## Le chargement a nécessité le package : pheatmap
## Le chargement a nécessité le package : RColorBrewer
## Le chargement a nécessité le package : genefilter
##
## Attachement du package : 'genefilter'
## Les objets suivants sont masqués depuis 'package:MatrixGenerics':
##
## rowSds, rowVars
## Les objets suivants sont masqués depuis 'package:matrixStats':
##
## rowSds, rowVars
## Le chargement a nécessité le package : gplots
##
## Attachement du package : 'gplots'
## L'objet suivant est masqué depuis 'package:IRanges':
##
## space
## L'objet suivant est masqué depuis 'package:S4Vectors':
##
## space
## L'objet suivant est masqué depuis 'package:stats':
##
## lowess
## Le chargement a nécessité le package : vegan
## Le chargement a nécessité le package : permute
##
## Attachement du package : 'permute'
## L'objet suivant est masqué depuis 'package:devtools':
##
## check
## Le chargement a nécessité le package : lattice
## This is vegan 2.6-4
## Le chargement a nécessité le package : dplyr
##
## Attachement du package : 'dplyr'
## L'objet suivant est masqué depuis 'package:Biobase':
##
## combine
## L'objet suivant est masqué depuis 'package:matrixStats':
##
## count
## Les objets suivants sont masqués depuis 'package:GenomicRanges':
##
## intersect, setdiff, union
## L'objet suivant est masqué depuis 'package:GenomeInfoDb':
##
## intersect
## Les objets suivants sont masqués depuis 'package:IRanges':
##
## collapse, desc, intersect, setdiff, slice, union
## Les objets suivants sont masqués depuis 'package:S4Vectors':
##
## first, intersect, rename, setdiff, setequal, union
## Les objets suivants sont masqués depuis 'package:BiocGenerics':
##
## combine, intersect, setdiff, union
## Les objets suivants sont masqués depuis 'package:stats':
##
## filter, lag
## Les objets suivants sont masqués depuis 'package:base':
##
## intersect, setdiff, setequal, union
## Le chargement a nécessité le package : limma
## Warning: le package 'limma' a été compilé avec la version R 4.1.3
##
## Attachement du package : 'limma'
## L'objet suivant est masqué depuis 'package:DESeq2':
##
## plotMA
## L'objet suivant est masqué depuis 'package:BiocGenerics':
##
## plotMA
#BiocManager::install('tximport', force = TRUE)
#BiocManager::install('apeglm')
#BiocManager::install('ashr')
#BiocManager::install("EnhancedVolcano")
#BiocManager::install("arrayQualityMetrics")
if (!require(devtools)) install.packages("devtools")
devtools::install_github("yanlinlin82/ggvenn")
## Skipping install of 'ggvenn' from a github remote, the SHA1 (25fd3b55) has not changed since last install.
## Use `force = TRUE` to force installation
library("adegenet")
library('ggvenn')
## Le chargement a nécessité le package : grid
library('tximport')
library('apeglm')
library('ashr')
library('EnhancedVolcano')
## Registered S3 methods overwritten by 'ggalt':
## method from
## grid.draw.absoluteGrob ggplot2
## grobHeight.absoluteGrob ggplot2
## grobWidth.absoluteGrob ggplot2
## grobX.absoluteGrob ggplot2
## grobY.absoluteGrob ggplot2
library('BiocManager')
source_url("https://raw.githubusercontent.com/obigriffith/biostar-tutorials/master/Heatmaps/heatmap.3.R")
## ℹ SHA-1 hash of file is "015fc0457e61e3e93a903e69a24d96d2dac7b9fb"
# Working environment and data loading
scriptPath<-dirname(rstudioapi::getSourceEditorContext()$path)
setwd(scriptPath)
#candidateGenes<-read.csv('candidateGenes.csv',header=T,sep=',')
samplesSaccharina<-read.table('saccharinaDesignMulti.txt',header=T)
samplesHedophylum<-read.table('hedophylumDesignMulti.txt',header=T)
dataPath<-'/Users/mmeynadier/Documents/kelpProject/kallistoOutput'
outputPath<-'/Users/mmeynadier/Documents/kelpProject/DESeq2Output'
#setwd(dataPath)
# DDS object
# If data from kallisto
tx2geneSaccharina<-read.table('Saccharina_tx2gene',header=T)
tx2geneHedophylum<-read.table('Hedophylum_tx2gene',header=T)
#
# # Data importation - txImport
setwd(dataPath)
filesSaccharina<-paste0(samplesSaccharina$sample)
txiSaccharina<-tximport(files = filesSaccharina,type='kallisto',tx2gene = tx2geneSaccharina)
## Note: importing `abundance.h5` is typically faster than `abundance.tsv`
## reading in files with read_tsv
## 1 2 3 4 5 6 7 8
## transcripts missing from tx2gene: 8998
## summarizing abundance
## summarizing counts
## summarizing length
filesHedophylum<-paste0(samplesHedophylum$sample)
txiHedophylum<-tximport(files = filesHedophylum,type='kallisto',tx2gene = tx2geneHedophylum)
## Note: importing `abundance.h5` is typically faster than `abundance.tsv`
## reading in files with read_tsv
## 1 2 3 4 5 6 7 8 9
## transcripts missing from tx2gene: 9442
## summarizing abundance
## summarizing counts
## summarizing length
ddsSaccharina<-DESeqDataSetFromTximport(txiSaccharina,colData=samplesSaccharina,design= ~treatment + mesocosm)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## using counts and average transcript lengths from tximport
ddsHedophylum<-DESeqDataSetFromTximport(txiHedophylum,colData=samplesHedophylum,design= ~treatment + mesocosm)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## using counts and average transcript lengths from tximport
# pre-filtering
keepSaccharina <- rowSums(counts(ddsSaccharina)) >= 10
ddsSaccharina <- ddsSaccharina[keepSaccharina,]
keepHedophylum <- rowSums(counts(ddsHedophylum)) >= 10
ddsHedophylum <- ddsHedophylum[keepHedophylum,]
# Differential expression analysis
ddsSaccharina<-DESeq(ddsSaccharina)
## estimating size factors
## using 'avgTxLength' from assays(dds), correcting for library size
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
cbind(resultsNames(ddsSaccharina))
## [,1]
## [1,] "Intercept"
## [2,] "treatment_T1_vs_C"
## [3,] "treatment_T2_vs_C"
## [4,] "treatment_T3_vs_C"
## [5,] "mesocosm_M1_vs_M0"
## [6,] "mesocosm_M2_vs_M0"
ddsHedophylum<-DESeq(ddsHedophylum)
## estimating size factors
## using 'avgTxLength' from assays(dds), correcting for library size
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
cbind(resultsNames(ddsHedophylum))
## [,1]
## [1,] "Intercept"
## [2,] "treatment_T1_vs_C"
## [3,] "treatment_T2_vs_C"
## [4,] "treatment_T3_vs_C"
## [5,] "mesocosm_M1_vs_M0"
## [6,] "mesocosm_M2_vs_M0"
# Exploring the results - Saccharina
S_C_vs_T1 <- results(ddsSaccharina,contrast=c("treatment", "C", "T1"))
S_C_vs_T2 <- results(ddsSaccharina,contrast=c("treatment", "C", "T2"))
S_C_vs_T3 <- results(ddsSaccharina,contrast=c("treatment", "C", "T3"))
S_T1_vs_T2 <- results(ddsSaccharina,contrast=c("treatment", "T1", "T2"))
S_T1_vs_T3 <- results(ddsSaccharina,contrast=c("treatment", "T1", "T3"))
S_T2_vs_T3 <- results(ddsSaccharina,contrast=c("treatment", "T2", "T3"))
DESeq2::plotMA(S_C_vs_T1,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T1")
DESeq2::plotMA(S_C_vs_T2,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T2")
DESeq2::plotMA(S_C_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T3")
DESeq2::plotMA(S_T1_vs_T2,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT1 vs T2")
DESeq2::plotMA(S_T1_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT1 vs T3")
DESeq2::plotMA(S_T2_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT2 vs T3")
vsdSaccharina <- vst(ddsSaccharina, blind=T)
meanSdPlot(assay(vsdSaccharina))
ntd <- normTransform(ddsSaccharina)
meanSdPlot(assay(ntd))
select <- order(rowMeans(counts(ddsSaccharina,normalized=TRUE)),
decreasing=TRUE)[1:20]
df <- as.data.frame(colData(ddsSaccharina)[,c("treatment","mesocosm")])
pheatmap(assay(vsdSaccharina)[select,], cluster_rows=FALSE, show_rownames=F,
cluster_cols=FALSE, annotation_col=df)
pcaData <- plotPCA(vsdSaccharina, intgroup=c("treatment", "mesocosm"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(PC1, PC2, color=treatment, shape=mesocosm)) +
geom_point(size=3) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed()
sampleDists <- dist(t(assay(vsdSaccharina)))
library("RColorBrewer")
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsdSaccharina$treatment, vsdSaccharina$mesocosm, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
clustering_distance_rows=sampleDists,
clustering_distance_cols=sampleDists,
col=colors)
count_tab_assay <- assay(vsdSaccharina)
dist_tab_assay <- dist(t(count_tab_assay),method="euclidian")
adonis(data=samplesSaccharina,dist_tab_assay ~ treatment + mesocosm, method="euclidian")
## 'adonis' will be deprecated: use 'adonis2' instead
## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## treatment 3 97266 32422 0.92564 0.39825 0.654
## mesocosm 2 76913 38456 1.09792 0.31492 0.326
## Residuals 2 70053 35027 0.28683
## Total 7 244232 1.00000
##
## $call
## adonis(formula = dist_tab_assay ~ treatment + mesocosm, data = samplesSaccharina,
## method = "euclidian")
##
## $coefficients
## NULL
##
## $coef.sites
## [,1] [,2] [,3] [,4] [,5]
## (Intercept) 280.7711645 288.85452 225.2320469 165.182879 207.888057
## treatment1 -103.7689116 -116.40654 56.5620319 10.281629 52.238253
## treatment2 42.0223555 26.34908 37.1787404 -0.305203 -64.886478
## treatment3 2.2744865 63.95154 -4.6982486 -14.979211 -14.275662
## mesocosm1 93.0259208 83.91737 0.9539806 -175.464508 -1.099292
## mesocosm2 0.9088432 -78.62335 54.9685962 75.039373 48.816422
## [,6] [,7] [,8]
## (Intercept) 233.93560 215.95196 175.774567
## treatment1 40.69503 52.02059 85.352178
## treatment2 -104.60326 14.78854 43.297898
## treatment3 37.30763 30.87268 -180.206402
## mesocosm1 -28.43778 -34.88477 -35.883704
## mesocosm2 -39.64341 -31.78154 4.431836
##
## $f.perms
## [,1] [,2]
## [1,] 0.7432300 0.7290456
## [2,] 0.8848041 1.1393322
## [3,] 1.1729035 1.2457257
## [4,] 1.2843731 1.2531550
## [5,] 1.1661533 1.2386751
## [6,] 0.9762207 1.2699893
## [7,] 1.0383574 1.2328177
## [8,] 1.0142775 1.1383605
## [9,] 1.2440237 1.4470576
## [10,] 1.2909057 1.1648786
## [11,] 1.0868488 0.8842503
## [12,] 1.1366950 1.0308384
## [13,] 0.9472805 0.8951887
## [14,] 1.1935284 0.9320958
## [15,] 0.9051350 1.6214595
## [16,] 0.8705855 1.4068916
## [17,] 0.8772709 0.7506268
## [18,] 1.0002977 0.8710072
## [19,] 1.1587166 1.5219865
## [20,] 1.1156383 1.1566790
## [21,] 1.2259409 1.1489937
## [22,] 0.9715496 0.7464401
## [23,] 1.1631281 1.0388870
## [24,] 0.7450840 0.8273387
## [25,] 1.2484102 1.0943515
## [26,] 1.1758260 0.8503507
## [27,] 1.0971235 0.9871757
## [28,] 1.3676104 1.1847837
## [29,] 0.9825889 0.7885293
## [30,] 1.1789334 1.0870052
## [31,] 1.0679314 0.8120387
## [32,] 0.9873380 1.0807963
## [33,] 0.7624097 0.8718371
## [34,] 1.2316787 1.0346532
## [35,] 0.9632473 0.9803554
## [36,] 0.8271822 0.8622812
## [37,] 1.3027186 1.2669479
## [38,] 0.8032766 0.7908005
## [39,] 0.8237494 0.8833290
## [40,] 0.9242755 0.8554582
## [41,] 0.7904664 0.8779048
## [42,] 1.2834472 1.3115194
## [43,] 1.1772781 1.1070936
## [44,] 1.1272167 0.8038415
## [45,] 1.0304728 0.9896683
## [46,] 1.0591602 1.2287034
## [47,] 1.0498093 1.0008169
## [48,] 0.9332430 1.2599122
## [49,] 0.8032766 0.7908005
## [50,] 0.8957059 1.0526557
## [51,] 0.7305756 0.7245254
## [52,] 0.8660802 0.9463642
## [53,] 1.5278710 1.3253900
## [54,] 1.0423602 1.0765188
## [55,] 0.7081438 0.8523895
## [56,] 0.7713326 0.8220269
## [57,] 1.4107214 1.2090405
## [58,] 1.1053067 1.2695645
## [59,] 1.2251476 1.2113237
## [60,] 0.8723904 1.0441015
## [61,] 1.2206784 1.2832185
## [62,] 1.0108054 1.5236366
## [63,] 0.9650073 0.8593427
## [64,] 1.1643856 1.2840502
## [65,] 0.7380623 0.7132953
## [66,] 1.0475921 0.8191648
## [67,] 1.1238935 1.5055739
## [68,] 1.5088466 1.2608009
## [69,] 0.7431952 0.9440782
## [70,] 1.0435922 1.2091706
## [71,] 0.8711250 1.3910917
## [72,] 1.2855191 1.0244809
## [73,] 0.8982184 0.7970368
## [74,] 1.1971719 1.0178351
## [75,] 1.0432567 1.1602877
## [76,] 1.3054402 1.0781414
## [77,] 0.7702279 0.9028931
## [78,] 0.8252817 1.2206159
## [79,] 0.8017169 0.9839279
## [80,] 0.8868576 0.9792648
## [81,] 1.1484534 1.1171440
## [82,] 1.2024761 1.2257083
## [83,] 0.8468687 1.2027705
## [84,] 0.7996538 0.6782247
## [85,] 0.8845318 1.1090785
## [86,] 0.8220240 0.9205147
## [87,] 0.8561634 1.0791637
## [88,] 0.8308943 0.7535672
## [89,] 1.0621480 0.8685895
## [90,] 1.1053458 1.5870095
## [91,] 0.8793322 0.6761034
## [92,] 1.0160962 1.0899453
## [93,] 0.8800792 0.8498708
## [94,] 1.0090250 1.0894036
## [95,] 1.0118802 0.8101927
## [96,] 1.1025787 1.0685203
## [97,] 1.0682680 1.2064726
## [98,] 1.3847534 1.0380336
## [99,] 1.2355100 1.2258142
## [100,] 0.9849165 0.9670736
## [101,] 0.8002744 0.9078132
## [102,] 1.0240818 1.3668708
## [103,] 0.9163828 0.8301982
## [104,] 0.8049490 0.6826231
## [105,] 1.0627728 1.0540134
## [106,] 0.9590478 0.7436096
## [107,] 1.2042549 1.2619442
## [108,] 1.0078700 0.8860917
## [109,] 1.4491191 1.0473471
## [110,] 1.3020159 1.1153027
## [111,] 0.9101772 1.0437645
## [112,] 1.6002879 1.2167646
## [113,] 0.5996318 0.7863096
## [114,] 0.7556862 0.7193096
## [115,] 0.7380623 0.7132953
## [116,] 1.1196108 1.2610868
## [117,] 0.9626846 0.8927032
## [118,] 0.8753691 1.0215512
## [119,] 0.8305596 0.9339126
## [120,] 0.9176752 0.7706018
## [121,] 0.7279569 0.9229149
## [122,] 1.1920712 1.1086911
## [123,] 0.7839264 1.1033396
## [124,] 1.0266995 0.9842949
## [125,] 0.8529498 0.7770916
## [126,] 0.8815990 1.0824962
## [127,] 0.9137536 0.8224032
## [128,] 1.1234907 0.8765672
## [129,] 0.9221499 0.9523065
## [130,] 0.6428772 0.7645898
## [131,] 0.7729761 1.1197652
## [132,] 0.9022983 0.7868282
## [133,] 1.3427224 1.6489040
## [134,] 1.1581972 1.0881731
## [135,] 1.1359992 1.0468282
## [136,] 1.1849999 1.0410305
## [137,] 1.2552694 0.8653186
## [138,] 1.6232882 1.3300176
## [139,] 0.7467571 0.8240233
## [140,] 1.0758731 0.9952841
## [141,] 1.0867904 1.0216499
## [142,] 0.8222308 0.9948299
## [143,] 0.9984302 0.8293392
## [144,] 1.4338513 1.1289216
## [145,] 1.1352970 1.0235945
## [146,] 1.3385660 1.4283348
## [147,] 1.2739661 1.2699985
## [148,] 1.0487565 1.0036733
## [149,] 0.9862880 1.0067037
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## [622,] 1.1344139 1.0717874
## [623,] 0.9179360 0.9762059
## [624,] 0.9025292 0.8379018
## [625,] 0.8892221 1.0561745
## [626,] 0.8260558 0.8722955
## [627,] 1.1919093 0.9364194
## [628,] 0.8456699 0.8925614
## [629,] 0.8456699 0.8925614
## [630,] 0.8630533 1.3296351
## [631,] 1.0992838 1.3056771
## [632,] 0.7840449 0.7876083
## [633,] 0.7978897 0.7436696
## [634,] 0.9140999 1.0171548
## [635,] 0.9365634 0.9335736
## [636,] 0.8464276 0.6614314
## [637,] 1.1892676 1.1203350
## [638,] 1.0612980 1.1152750
## [639,] 1.1013617 0.9647660
## [640,] 1.1226515 1.0195536
## [641,] 0.7986581 0.7975907
## [642,] 1.1555691 1.0282626
## [643,] 1.1439279 1.1729466
## [644,] 0.9724622 0.9597829
## [645,] 1.0699198 1.0196484
## [646,] 1.6483921 1.1446084
## [647,] 0.7716332 0.7202555
## [648,] 1.0885960 1.0582116
## [649,] 1.1210231 1.1922589
## [650,] 1.1688021 1.3164127
## [651,] 1.3426549 1.2254040
## [652,] 1.1983633 1.1582813
## [653,] 0.7756160 0.8120904
## [654,] 1.2840253 1.4277105
## [655,] 0.9954563 1.0738855
## [656,] 0.9695817 1.0784741
## [657,] 0.8891768 0.8525692
## [658,] 1.1947082 1.5932502
## [659,] 1.2236204 1.0613673
## [660,] 0.8489543 0.7770526
## [661,] 0.9894320 0.8771653
## [662,] 0.8599440 0.9360397
## [663,] 1.0447079 1.1098183
## [664,] 1.3369120 1.2187638
## [665,] 1.2835229 1.1956830
## [666,] 0.9025292 0.8379018
## [667,] 0.9575264 0.8725267
## [668,] 1.0401908 1.2137431
## [669,] 1.1897222 1.2413359
## [670,] 1.2705004 1.3309396
## [671,] 1.1429473 1.3924462
## [672,] 1.1949040 0.8680605
## [673,] 0.8503457 0.8844881
## [674,] 1.2184506 1.2252641
## [675,] 1.0104799 0.9991337
## [676,] 1.0359170 0.9270151
## [677,] 0.9437935 0.8469207
## [678,] 0.7456392 0.9714163
## [679,] 0.8458905 0.6764756
## [680,] 0.9830624 1.0295725
## [681,] 0.9326259 0.9495122
## [682,] 0.8370918 0.9216414
## [683,] 0.9038528 1.0769603
## [684,] 0.9450050 0.6586791
## [685,] 0.9342991 0.9397451
## [686,] 0.9839197 0.9247637
## [687,] 0.9633922 1.1683682
## [688,] 1.2594626 1.0777730
## [689,] 0.9060723 0.9973914
## [690,] 0.8630356 0.7010245
## [691,] 1.0905454 0.8019209
## [692,] 1.3500625 1.1831205
## [693,] 0.9275484 0.9602443
## [694,] 1.1082017 0.9706791
## [695,] 1.0197455 0.8823804
## [696,] 0.7389218 0.9588984
## [697,] 0.9712378 0.8953497
## [698,] 0.9647791 0.9653939
## [699,] 1.0599807 1.2112246
## [700,] 1.3676255 1.2578093
## [701,] 1.0140709 1.2175774
## [702,] 1.0071987 0.9540930
## [703,] 0.7637382 0.6837297
## [704,] 1.1925366 1.0606423
## [705,] 1.1168212 0.7459893
## [706,] 0.9716694 0.7465534
## [707,] 1.0463054 0.8374901
## [708,] 1.2799074 1.1725563
## [709,] 1.0257467 0.8481767
## [710,] 0.9901180 0.9197178
## [711,] 0.9223333 1.3916730
## [712,] 0.9588444 0.8362203
## [713,] 1.2236864 1.0115105
## [714,] 0.9862880 1.0067037
## [715,] 1.2355342 0.9832187
## [716,] 1.0542445 1.3450124
## [717,] 0.9191070 0.9340009
## [718,] 0.8246598 0.7951642
## [719,] 1.1613623 1.2454465
## [720,] 1.0786600 0.9305868
## [721,] 1.0078559 0.9467545
## [722,] 0.6973789 0.5630912
## [723,] 0.7576445 0.8996450
## [724,] 0.6383500 0.8100499
## [725,] 1.1729035 1.2457257
## [726,] 0.9026883 0.9640071
## [727,] 0.9656666 0.7172135
## [728,] 1.1456106 1.1117206
## [729,] 1.2889299 1.3348074
## [730,] 1.0337772 0.9605482
## [731,] 1.1369760 1.3041239
## [732,] 1.2499462 1.3430898
## [733,] 1.0480965 0.8896668
## [734,] 1.0616841 1.1896894
## [735,] 1.2501589 1.1953851
## [736,] 0.6743576 0.7382286
## [737,] 1.0450767 1.0164158
## [738,] 0.7847498 1.0414661
## [739,] 1.1852873 1.0481159
## [740,] 0.7940223 0.8699340
## [741,] 0.9998463 0.9464617
## [742,] 0.9377773 1.1488593
## [743,] 0.8195256 1.1715121
## [744,] 0.7958550 1.0102027
## [745,] 0.8547373 0.7024478
## [746,] 0.7013885 0.8169594
## [747,] 0.8386591 0.6406019
## [748,] 1.0831210 1.3477893
## [749,] 1.1599025 1.4624492
## [750,] 0.7410526 0.8883296
## [751,] 0.6226599 0.7855845
## [752,] 1.2605829 1.1397543
## [753,] 0.9418263 1.2606525
## [754,] 1.1570685 0.9345075
## [755,] 0.7235885 0.9249239
## [756,] 0.8442062 1.2380096
## [757,] 0.9747304 1.0325711
## [758,] 0.7176625 0.7917586
## [759,] 1.1423713 0.9246163
## [760,] 0.8753662 0.9854460
## [761,] 0.8577762 0.8029649
## [762,] 0.8982930 1.0751731
## [763,] 0.9899512 0.8230409
## [764,] 0.7763803 0.7338416
## [765,] 0.9029047 1.1672295
## [766,] 0.9835032 0.9404684
## [767,] 0.9885856 0.9714552
## [768,] 0.9471229 1.2169136
## [769,] 1.2785701 0.9524152
## [770,] 0.9141406 1.1843143
## [771,] 1.0331047 1.0780306
## [772,] 1.0091050 1.1564527
## [773,] 0.9653938 0.7612914
## [774,] 1.1983633 1.1582813
## [775,] 1.0184897 1.1219881
## [776,] 1.1849674 0.9792517
## [777,] 1.0724452 1.0876125
## [778,] 0.7607048 1.0458607
## [779,] 0.8118340 0.7753697
## [780,] 0.7926029 0.8842592
## [781,] 1.2584466 0.9148043
## [782,] 0.8683132 0.9588256
## [783,] 0.7723636 0.6358177
## [784,] 0.9573253 0.8708658
## [785,] 0.8090954 0.9098716
## [786,] 0.8551142 0.7327804
## [787,] 0.7730531 0.6279720
## [788,] 1.0202442 1.2785571
## [789,] 1.0327221 0.9179604
## [790,] 0.8747842 0.9425548
## [791,] 0.7380623 0.7132953
## [792,] 1.1295610 0.8421962
## [793,] 1.2168379 1.0689523
## [794,] 1.0352350 0.9623957
## [795,] 1.2600464 1.1982794
## [796,] 0.7624101 0.8434006
## [797,] 1.0255241 1.1057440
## [798,] 0.9823391 0.8974667
## [799,] 1.5375121 1.2828328
## [800,] 1.3393128 1.0965608
## [801,] 1.2663164 0.9499725
## [802,] 0.8266393 1.0055255
## [803,] 0.8802133 0.6327773
## [804,] 1.3690518 0.9622991
## [805,] 1.0480965 0.8896668
## [806,] 0.9219862 0.7789198
## [807,] 1.1109447 1.1734071
## [808,] 1.2670736 1.2049195
## [809,] 1.3299877 1.1620649
## [810,] 1.1174778 1.2431366
## [811,] 0.9355962 0.9871558
## [812,] 0.9541660 0.9089694
## [813,] 1.3208608 1.4059856
## [814,] 1.1086845 1.2136228
## [815,] 0.8941419 0.7473888
## [816,] 0.9069892 0.7656913
## [817,] 1.5836036 1.1936529
## [818,] 1.2258614 1.1827716
## [819,] 0.8740992 0.8421476
## [820,] 1.0286830 0.9508871
## [821,] 0.7901561 0.8429813
## [822,] 1.0343448 0.9716282
## [823,] 0.9541204 1.2330736
## [824,] 1.1785130 0.8659577
## [825,] 1.0051538 1.1639755
## [826,] 0.8173289 0.9792182
## [827,] 0.9821411 1.1872049
## [828,] 1.0618377 0.9223901
## [829,] 1.1779920 1.2033756
## [830,] 0.9803460 0.9867396
## [831,] 1.0006820 0.6866511
## [832,] 1.3224874 1.1979515
## [833,] 1.2159931 1.1392421
## [834,] 0.7646375 0.8167194
## [835,] 1.2265721 1.2525592
## [836,] 0.8971072 0.8668499
## [837,] 0.7428042 0.9452459
## [838,] 0.9147588 0.9500288
## [839,] 0.9381240 0.8831160
## [840,] 0.8995725 0.8765970
## [841,] 1.1680849 1.0800712
## [842,] 1.1022839 1.3755116
## [843,] 0.9301778 1.1993184
## [844,] 1.1696578 0.9483810
## [845,] 1.4115457 1.1919290
## [846,] 1.2843731 1.2531550
## [847,] 0.8392968 0.9588794
## [848,] 0.7646617 0.7193727
## [849,] 0.6989968 0.8639347
## [850,] 1.1184452 1.1422320
## [851,] 0.9766480 0.7183557
## [852,] 1.4380954 1.1810378
## [853,] 1.2747228 1.3674014
## [854,] 0.7360438 1.1107325
## [855,] 0.9818310 0.8848299
## [856,] 1.1824185 1.2727955
## [857,] 0.8222603 0.8512002
## [858,] 1.0414436 1.0754325
## [859,] 1.0896582 0.9633317
## [860,] 1.0163045 1.1786328
## [861,] 1.1059339 1.0031338
## [862,] 1.4457105 1.2167468
## [863,] 1.2238554 0.8455165
## [864,] 0.9015349 0.8340322
## [865,] 1.2101928 1.0595212
## [866,] 0.7947965 1.0051645
## [867,] 0.9711559 1.0583933
## [868,] 1.2742943 0.9274953
## [869,] 0.8932214 0.8101462
## [870,] 0.8254213 0.7749334
## [871,] 0.9980389 0.8934945
## [872,] 1.1541856 1.1387317
## [873,] 1.0827496 1.3283138
## [874,] 1.1190131 1.1204425
## [875,] 1.3474126 1.0480663
## [876,] 1.0793391 0.9705532
## [877,] 0.9202464 1.0248320
## [878,] 1.0865528 1.1917000
## [879,] 1.2780054 1.1404493
## [880,] 1.2483133 1.4718395
## [881,] 1.4724445 1.0296665
## [882,] 1.2211450 1.2689986
## [883,] 1.1921335 1.5629528
## [884,] 0.8258451 1.0859529
## [885,] 0.8795712 0.7206073
## [886,] 1.1044053 1.1050084
## [887,] 0.9678294 0.8219174
## [888,] 1.4133306 1.2855542
## [889,] 1.2097430 1.0742239
## [890,] 0.8486594 0.8394173
## [891,] 0.9594908 0.9701556
## [892,] 1.0583865 1.1174743
## [893,] 0.8491222 0.7898913
## [894,] 1.2513737 0.9627636
## [895,] 1.2600464 1.1982794
## [896,] 0.9128490 1.0015652
## [897,] 0.7376282 0.7072383
## [898,] 0.8025521 0.9528601
## [899,] 1.0603430 1.1301982
## [900,] 1.2470593 0.9093931
## [901,] 1.1016222 1.2113094
## [902,] 0.9332430 1.2599122
## [903,] 1.0243274 0.9201555
## [904,] 1.3470673 1.3009010
## [905,] 0.9193640 1.3476005
## [906,] 0.9249031 0.6875378
## [907,] 0.6870072 1.0129464
## [908,] 0.9403708 1.2961274
## [909,] 1.3819979 1.4264289
## [910,] 1.0769142 0.8394575
## [911,] 1.0892338 1.5086742
## [912,] 0.9882986 1.3066427
## [913,] 1.2082905 1.1180536
## [914,] 0.8907446 1.0468391
## [915,] 0.9432034 0.8943794
## [916,] 1.1108441 0.9195055
## [917,] 0.8090954 0.9098716
## [918,] 0.9980398 1.2117994
## [919,] 0.9139084 0.9695471
## [920,] 1.3053631 1.2149067
## [921,] 0.7625342 0.8029408
## [922,] 0.8396087 0.8604144
## [923,] 1.0420899 0.7307147
## [924,] 1.1021842 1.5490267
## [925,] 1.0378237 1.2248240
## [926,] 0.8283966 0.9752297
## [927,] 1.0553328 1.0168520
## [928,] 0.8521390 0.7252865
## [929,] 1.0071513 1.1751846
## [930,] 1.0706318 0.7690173
## [931,] 0.9402211 0.7314605
## [932,] 1.0904697 1.2214437
## [933,] 0.8241052 0.8168143
## [934,] 0.7489591 0.6553583
## [935,] 1.1723204 0.9326249
## [936,] 1.0214597 0.8979651
## [937,] 0.8540057 0.8725422
## [938,] 1.1809822 1.3292428
## [939,] 1.3346412 1.2580995
## [940,] 0.8503184 1.0214666
## [941,] 0.9532520 0.9443186
## [942,] 1.1372795 0.8092274
## [943,] 0.8540057 0.8725422
## [944,] 1.1209882 1.3479401
## [945,] 0.8704251 0.9494264
## [946,] 0.9929162 1.0708482
## [947,] 0.9703802 1.0891121
## [948,] 1.3564547 1.6125858
## [949,] 1.1930031 1.4285045
## [950,] 1.1323496 0.9164400
## [951,] 1.0244100 0.9526910
## [952,] 0.9862022 0.7929781
## [953,] 0.7989311 0.7140332
## [954,] 0.8665568 1.1252541
## [955,] 1.2893626 1.2430979
## [956,] 0.7716332 0.7202555
## [957,] 1.0307749 0.8801906
## [958,] 0.8345243 0.8530165
## [959,] 1.1931884 1.1447288
## [960,] 0.6705743 0.7560912
## [961,] 0.8556224 1.1890731
## [962,] 0.7352058 0.7208831
## [963,] 1.2928420 1.3402226
## [964,] 0.7667515 1.1046039
## [965,] 0.9350848 0.7498842
## [966,] 1.1190131 1.1204425
## [967,] 1.1734070 0.9793174
## [968,] 1.0973462 1.3997457
## [969,] 1.1621292 1.2062480
## [970,] 0.9090665 0.6780744
## [971,] 1.1439279 1.1729466
## [972,] 1.2094013 0.9341207
## [973,] 1.5069546 0.9605939
## [974,] 0.8092907 0.9058913
## [975,] 1.0006820 0.6866511
## [976,] 1.3000739 1.1380421
## [977,] 1.4996559 1.0554235
## [978,] 0.7494352 0.5976019
## [979,] 0.6918273 0.8177407
## [980,] 0.7646617 0.7193727
## [981,] 1.1043441 0.8327898
## [982,] 0.8937388 0.9144560
## [983,] 1.2279257 1.2347776
## [984,] 0.7885737 0.8453549
## [985,] 0.8135027 0.8921524
## [986,] 0.9257986 0.9908109
## [987,] 1.0042614 0.8501079
## [988,] 1.1751164 0.9103017
## [989,] 1.1032416 1.0353574
## [990,] 1.3899644 1.0960010
## [991,] 0.9310316 0.9019479
## [992,] 1.3536862 0.8659661
## [993,] 1.4865358 0.9605712
## [994,] 1.1189163 0.8178323
## [995,] 1.1954913 1.6015319
## [996,] 1.0902238 1.0709330
## [997,] 0.9541149 0.7068541
## [998,] 0.8593658 1.0534316
## [999,] 1.2783481 0.9189979
##
## $model.matrix
## (Intercept) treatment1 treatment2 treatment3 mesocosm1 mesocosm2
## 1 1 1 0 0 -1 -1
## 2 1 1 0 0 0 1
## 3 1 -1 -1 -1 -1 -1
## 4 1 1 0 0 1 0
## 5 1 0 1 0 -1 -1
## 6 1 0 1 0 0 1
## 7 1 -1 -1 -1 0 1
## 8 1 0 0 1 0 1
##
## $terms
## dist_tab_assay ~ treatment + mesocosm
## attr(,"variables")
## list(dist_tab_assay, treatment, mesocosm)
## attr(,"factors")
## treatment mesocosm
## dist_tab_assay 0 0
## treatment 1 0
## mesocosm 0 1
## attr(,"term.labels")
## [1] "treatment" "mesocosm"
## attr(,"order")
## [1] 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"
anova(betadisper(dist_tab_assay,samplesSaccharina$treatment))
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 3 18627.4 6209.1 188.23 9.254e-05 ***
## Residuals 4 131.9 33.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(betadisper(dist_tab_assay,samplesSaccharina$mesocosm))
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 20684.5 10342.3 8.8147 0.02295 *
## Residuals 5 5866.5 1173.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Hedophylum
H_C_vs_T1 <- results(ddsHedophylum,contrast=c("treatment", "C", "T1"))
H_C_vs_T2 <- results(ddsHedophylum,contrast=c("treatment", "C", "T2"))
H_C_vs_T3 <- results(ddsHedophylum,contrast=c("treatment", "C", "T3"))
H_T1_vs_T2 <- results(ddsHedophylum,contrast=c("treatment", "T1", "T2"))
H_T1_vs_T3 <- results(ddsHedophylum,contrast=c("treatment", "T1", "T3"))
H_T2_vs_T3 <- results(ddsHedophylum,contrast=c("treatment", "T2", "T3"))
DESeq2::plotMA(H_C_vs_T1,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T1")
DESeq2::plotMA(H_C_vs_T2,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T2")
DESeq2::plotMA(H_C_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T3")
DESeq2::plotMA(H_T1_vs_T2,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT1 vs T2")
DESeq2::plotMA(H_T1_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT1 vs T3")
DESeq2::plotMA(H_T2_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT2 vs T3")
vsdHedophylum <- vst(ddsHedophylum, blind=T)
meanSdPlot(assay(vsdHedophylum))
ntd <- normTransform(ddsHedophylum)
meanSdPlot(assay(ntd))
select <- order(rowMeans(counts(ddsHedophylum,normalized=TRUE)),
decreasing=TRUE)[1:20]
df <- as.data.frame(colData(ddsHedophylum)[,c("treatment","mesocosm")])
pheatmap(assay(vsdHedophylum)[select,], cluster_rows=FALSE, show_rownames=F,
cluster_cols=FALSE, annotation_col=df)
pcaData <- plotPCA(vsdHedophylum, intgroup=c("treatment", "mesocosm"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(PC1, PC2, color=treatment, shape=mesocosm)) +
geom_point(size=3) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed()
sampleDists <- dist(t(assay(vsdHedophylum)))
library("RColorBrewer")
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsdHedophylum$treatment, vsdHedophylum$mesocosm, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
clustering_distance_rows=sampleDists,
clustering_distance_cols=sampleDists,
col=colors)
count_tab_assay <- assay(vsdHedophylum)
dist_tab_assay <- dist(t(count_tab_assay),method="euclidian")
adonis(data=samplesHedophylum,dist_tab_assay ~ treatment + mesocosm, method="euclidian")
## 'adonis' will be deprecated: use 'adonis2' instead
## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## treatment 3 85025 28342 1.3970 0.46892 0.037 *
## mesocosm 2 35431 17716 0.8732 0.19541 0.738
## Residuals 3 60864 20288 0.33567
## Total 8 181320 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $call
## adonis(formula = dist_tab_assay ~ treatment + mesocosm, data = samplesHedophylum,
## method = "euclidian")
##
## $coefficients
## NULL
##
## $coef.sites
## [,1] [,2] [,3] [,4] [,5] [,6]
## (Intercept) 191.970302 206.91199 195.443517 167.888032 182.76783 200.17806
## treatment1 9.572277 -126.89949 71.802839 2.449421 60.40426 31.59877
## treatment2 18.252854 36.45322 -22.585880 -58.462241 -100.95993 14.31881
## treatment3 32.415564 65.13722 -3.577885 52.932541 10.26161 18.56881
## mesocosm1 51.196095 60.61905 -79.843866 41.326635 -13.90914 47.31424
## mesocosm2 -32.394679 -36.34698 40.029982 -70.070501 44.16262 -21.16900
## [,7] [,8] [,9]
## (Intercept) 200.762289 170.24365 183.824138
## treatment1 70.429605 23.74341 -81.806451
## treatment2 1.102666 37.37274 11.353813
## treatment3 -119.739339 -59.07100 26.012012
## mesocosm1 -20.809615 39.48978 -36.459693
## mesocosm2 55.088379 -64.34184 7.663401
##
## $f.perms
## [,1] [,2]
## [1,] 1.1321430 1.1478069
## [2,] 1.1768598 1.2069534
## [3,] 0.7679314 0.8154228
## [4,] 1.0426125 0.8992346
## [5,] 0.9389493 0.9498231
## [6,] 0.9298261 0.9035579
## [7,] 0.8098218 1.0363176
## [8,] 0.9830413 1.1401417
## [9,] 1.0302974 1.1169251
## [10,] 1.1228002 1.4044043
## [11,] 1.1133002 1.2352425
## [12,] 1.3295294 1.2563451
## [13,] 0.7888683 0.9158639
## [14,] 0.8975459 1.0358257
## [15,] 1.2805290 1.4992478
## [16,] 1.7139342 1.1804166
## [17,] 0.7883888 0.9442758
## [18,] 0.9357620 0.9991741
## [19,] 1.0470044 1.0456297
## [20,] 0.8523837 0.7219771
## [21,] 0.9605456 1.0352997
## [22,] 0.8674407 1.0406762
## [23,] 1.0166021 1.2175166
## [24,] 1.0798034 1.1634646
## [25,] 1.0591468 0.8023110
## [26,] 1.0210727 0.9248374
## [27,] 0.9826087 1.0566507
## [28,] 1.0456339 1.3242426
## [29,] 1.1238083 1.4073074
## [30,] 0.8967747 0.8644003
## [31,] 1.1556371 1.1444146
## [32,] 0.8196177 0.9799965
## [33,] 0.8654207 0.9305841
## [34,] 0.8760509 0.7124325
## [35,] 0.9170494 0.8659192
## [36,] 1.2348309 1.3598966
## [37,] 1.1551596 1.0297459
## [38,] 0.9643762 1.1297041
## [39,] 0.8018289 0.8406504
## [40,] 1.0583864 0.9545308
## [41,] 0.8880879 0.8957572
## [42,] 0.9536734 1.4791547
## [43,] 1.1980362 1.0264376
## [44,] 1.3376535 1.0082282
## [45,] 0.9291911 0.7990284
## [46,] 1.0083973 0.8697106
## [47,] 0.8090737 0.9475025
## [48,] 0.8790558 0.9862338
## [49,] 0.7622075 0.8820869
## [50,] 1.1830938 1.1233251
## [51,] 0.7535054 0.8537869
## [52,] 1.0305653 0.8911411
## [53,] 0.8663311 1.2074987
## [54,] 1.1715601 1.0125958
## [55,] 0.9413860 1.0715632
## [56,] 1.0928102 1.1447335
## [57,] 0.8954735 1.3822699
## [58,] 0.8840963 1.0617144
## [59,] 0.8529456 0.8632499
## [60,] 0.9954049 0.7731098
## [61,] 1.2109346 1.2909709
## [62,] 0.7586511 0.7656413
## [63,] 1.2423205 0.8891954
## [64,] 0.9608148 0.9899144
## [65,] 0.7160354 0.7836810
## [66,] 0.8329551 0.8962249
## [67,] 1.1839347 0.9543077
## [68,] 0.9947941 1.2313402
## [69,] 1.2553788 1.3212578
## [70,] 0.9604279 0.9162303
## [71,] 0.7730499 0.8497553
## [72,] 0.9074982 1.3411797
## [73,] 1.3709411 1.3638178
## [74,] 0.9056427 0.8588335
## [75,] 1.0894069 1.3273672
## [76,] 0.9257093 1.0439677
## [77,] 1.0631529 1.3741720
## [78,] 1.3042598 1.2487848
## [79,] 0.9505359 1.0790541
## [80,] 0.8334724 1.1094210
## [81,] 1.3687242 1.0224612
## [82,] 1.2242029 1.3639801
## [83,] 0.8008879 0.6331905
## [84,] 0.8632762 0.8012244
## [85,] 0.9837822 0.8267875
## [86,] 0.8919451 1.2165967
## [87,] 0.9148583 0.9347649
## [88,] 0.9516479 0.9462418
## [89,] 1.0456848 1.0194297
## [90,] 1.0686699 1.1609035
## [91,] 0.7938900 0.8118043
## [92,] 0.9761500 0.8805557
## [93,] 1.1492592 1.1365248
## [94,] 1.0993219 1.0020559
## [95,] 0.9408588 0.8396738
## [96,] 1.2235383 1.0549834
## [97,] 1.3766895 1.4219283
## [98,] 1.4046392 1.1540395
## [99,] 0.8788570 1.0153963
## [100,] 0.9327845 0.9363734
## [101,] 1.2493635 1.5341089
## [102,] 1.3129810 1.1764534
## [103,] 1.0003739 0.7887688
## [104,] 0.8502987 0.6374124
## [105,] 0.7092845 0.8837503
## [106,] 1.0083275 0.7159487
## [107,] 1.1639144 1.0154079
## [108,] 0.7729203 1.1130778
## [109,] 1.0416923 0.9873126
## [110,] 1.1481458 0.8574244
## [111,] 0.9731228 1.3841536
## [112,] 0.8645598 0.8510824
## [113,] 0.8739803 0.8283760
## [114,] 1.0756493 0.9603158
## [115,] 0.8933945 0.7964993
## [116,] 0.8702253 0.9425040
## [117,] 0.9715425 1.0961130
## [118,] 0.8997336 0.8297867
## [119,] 0.8486854 0.8664839
## [120,] 0.9532945 0.8760506
## [121,] 0.9153811 0.9106972
## [122,] 0.8498745 0.8002030
## [123,] 1.1720971 1.4608453
## [124,] 0.9524544 0.8842743
## [125,] 1.2346152 0.9489737
## [126,] 0.9451158 1.0164001
## [127,] 1.1647111 1.0236726
## [128,] 0.9357620 0.9991741
## [129,] 0.7814634 0.7484747
## [130,] 1.1248061 1.2094772
## [131,] 1.0156953 0.8435066
## [132,] 1.0067029 1.1080189
## [133,] 1.1580440 1.3742864
## [134,] 1.1283534 1.4050284
## [135,] 1.0067065 1.1226071
## [136,] 0.8564675 1.0454951
## [137,] 0.8639700 1.1023001
## [138,] 1.0989870 0.9343298
## [139,] 1.1586961 0.8567860
## [140,] 1.1257810 1.1692717
## [141,] 0.9722789 1.2741987
## [142,] 0.9709301 0.9823045
## [143,] 0.9156182 1.0715666
## [144,] 0.8950958 0.9710177
## [145,] 1.1857579 0.8549827
## [146,] 0.7429287 0.9828542
## [147,] 0.9181432 0.9780481
## [148,] 1.2148197 1.4069299
## [149,] 1.6089770 1.4425610
## [150,] 0.9858647 1.1237383
## [151,] 0.8676710 0.9883919
## [152,] 0.9584422 1.2953791
## [153,] 1.1578946 1.0777763
## [154,] 0.6836829 0.7116429
## [155,] 0.9362382 1.0003138
## [156,] 0.9846352 1.0800894
## [157,] 0.9769793 0.9673234
## [158,] 1.0265959 1.5405520
## [159,] 0.8135127 0.8572326
## [160,] 0.9637425 0.8817547
## [161,] 1.1285507 0.9390238
## [162,] 0.8823821 0.8535617
## [163,] 0.9160060 1.0173016
## [164,] 1.3073173 1.0680859
## [165,] 0.6815780 0.7435194
## [166,] 0.8292531 0.8770520
## [167,] 1.0186360 1.4320146
## [168,] 1.2787233 1.0028932
## [169,] 1.0428518 1.1960491
## [170,] 1.0505185 1.0832669
## [171,] 0.6827824 0.7356287
## [172,] 1.2571688 1.2518794
## [173,] 1.0532460 0.9294864
## [174,] 1.0175737 1.1150419
## [175,] 1.1804554 1.0528014
## [176,] 1.1151583 1.1392423
## [177,] 1.0871661 1.0368060
## [178,] 1.0516271 0.8354947
## [179,] 0.8703188 0.7920236
## [180,] 1.0292662 1.0765568
## [181,] 1.0623514 1.0581511
## [182,] 1.0470351 1.2184314
## [183,] 0.8509324 0.9710055
## [184,] 1.0013540 1.1024390
## [185,] 1.0039659 1.0257625
## [186,] 0.9467356 1.2432149
## [187,] 1.0226498 0.8230784
## [188,] 0.9716739 0.8381398
## [189,] 1.4567044 1.6623065
## [190,] 1.0409332 0.8243988
## [191,] 0.8187516 0.9193714
## [192,] 0.9599542 0.9670411
## [193,] 0.7694235 0.8964334
## [194,] 1.0907160 1.3108160
## [195,] 1.0771941 0.7677632
## [196,] 1.0311620 0.9595008
## [197,] 1.0030246 0.9089198
## [198,] 0.9915413 0.8847789
## [199,] 0.8561704 0.9390079
## [200,] 1.1937645 1.2777902
## [201,] 0.8311430 0.6949905
## [202,] 1.0883230 1.2987503
## [203,] 1.2041436 1.3551213
## [204,] 0.9096595 0.8762071
## [205,] 0.9424596 0.7227529
## [206,] 0.9612526 0.9555168
## [207,] 1.1544519 0.7456926
## [208,] 0.9993065 0.7968524
## [209,] 0.7562301 0.8634059
## [210,] 0.9648673 1.1307306
## [211,] 0.7679499 0.6241095
## [212,] 1.2227571 1.2538627
## [213,] 1.1233643 1.5660015
## [214,] 0.9596831 0.8881022
## [215,] 1.1632753 1.2408017
## [216,] 1.1067871 0.7180954
## [217,] 0.8943187 0.7936945
## [218,] 1.1549726 1.0072878
## [219,] 1.1016265 1.0528801
## [220,] 1.1464685 0.8960963
## [221,] 0.9051245 0.8399874
## [222,] 0.8615505 0.8498742
## [223,] 0.8068474 0.8940458
## [224,] 0.9500371 1.1168517
## [225,] 0.9747147 0.9146335
## [226,] 1.2036857 0.9725780
## [227,] 0.8785497 1.2505481
## [228,] 0.9549476 1.2947114
## [229,] 1.1045035 1.3403603
## [230,] 0.8600674 1.0383599
## [231,] 1.2079979 1.2097727
## [232,] 0.9991739 1.1511395
## [233,] 1.0023838 0.7231558
## [234,] 0.8849399 1.1222081
## [235,] 1.1471401 1.2365287
## [236,] 0.9209687 0.9591670
## [237,] 0.8173010 0.7939589
## [238,] 1.2489605 0.7689500
## [239,] 1.1861024 1.7670872
## [240,] 0.9127739 1.1262892
## [241,] 0.9718655 0.8954053
## [242,] 0.8962517 1.1927010
## [243,] 1.1210724 1.0293753
## [244,] 1.0186864 1.0300678
## [245,] 1.3508999 0.9205401
## [246,] 0.9360839 1.0324331
## [247,] 0.9454118 1.2493363
## [248,] 0.8462073 0.8879132
## [249,] 0.9296855 0.8771342
## [250,] 0.8634529 0.9119721
## [251,] 0.8180891 1.0694144
## [252,] 0.7911812 1.1082801
## [253,] 1.1616582 1.1940241
## [254,] 0.9915413 0.8847789
## [255,] 1.0317164 0.9826199
## [256,] 0.8168796 0.8503762
## [257,] 0.9351618 0.8480762
## [258,] 0.9880451 0.9116198
## [259,] 1.0663113 0.9976555
## [260,] 0.9176870 1.3451447
## [261,] 1.2399519 1.2392268
## [262,] 1.0060880 0.8776186
## [263,] 1.1463907 0.9628819
## [264,] 1.3729702 1.1997167
## [265,] 0.8550984 0.9023318
## [266,] 0.9493941 0.8564136
## [267,] 0.8291360 1.2709573
## [268,] 1.1199211 1.0767553
## [269,] 1.0688121 1.0833522
## [270,] 1.1808651 1.2349104
## [271,] 1.1639742 0.8553593
## [272,] 1.0260881 1.1578823
## [273,] 1.2055489 1.1603130
## [274,] 0.8785564 0.9859631
## [275,] 0.9252167 0.8820381
## [276,] 0.8405549 0.9165234
## [277,] 1.0084241 0.8910140
## [278,] 0.9184574 1.2088233
## [279,] 0.8863328 0.8569929
## [280,] 0.8651371 1.0581083
## [281,] 1.1937645 1.2777902
## [282,] 1.0524278 1.1891937
## [283,] 0.8482905 0.7000076
## [284,] 0.9001521 0.9857275
## [285,] 0.9890211 0.9018613
## [286,] 0.8076705 0.7026646
## [287,] 1.0272877 1.1314553
## [288,] 1.0017008 1.0179948
## [289,] 0.9751587 1.0204795
## [290,] 0.9951927 1.2460441
## [291,] 0.9967365 1.0809937
## [292,] 1.3921859 1.1480222
## [293,] 1.1168703 1.0191542
## [294,] 1.3251006 0.9330490
## [295,] 0.9238942 0.9761967
## [296,] 0.9475890 1.0645299
## [297,] 0.8621190 0.9880179
## [298,] 0.9763378 1.1217518
## [299,] 0.8524938 0.8573144
## [300,] 0.7427235 0.7888599
## [301,] 0.9041208 0.8676603
## [302,] 0.8529047 0.9682133
## [303,] 1.0007891 1.1878069
## [304,] 0.9741420 0.9709770
## [305,] 0.8728055 0.8329856
## [306,] 0.9480186 0.7917997
## [307,] 1.0105254 1.1569782
## [308,] 1.0611893 0.9981138
## [309,] 0.7708199 0.6233211
## [310,] 0.7428875 0.7174601
## [311,] 1.0751558 1.2876721
## [312,] 1.0375486 1.1893969
## [313,] 0.8922240 1.0738888
## [314,] 0.9446686 1.2198720
## [315,] 0.8092949 1.0171676
## [316,] 0.9803403 1.3311444
## [317,] 1.0077777 1.1254429
## [318,] 0.9916693 1.2737319
## [319,] 0.7754984 0.8558117
## [320,] 1.2057261 1.7430890
## [321,] 0.9250789 1.2079512
## [322,] 0.9463195 1.0321621
## [323,] 1.0080048 1.1257891
## [324,] 0.9746142 1.0470514
## [325,] 0.8261137 1.0149974
## [326,] 1.2136525 0.8931304
## [327,] 1.1144541 1.1031061
## [328,] 0.8888282 0.6924013
## [329,] 0.8798143 0.9740271
## [330,] 1.1576085 0.9701269
## [331,] 0.7546442 0.9209153
## [332,] 1.1787194 1.2878420
## [333,] 1.2302020 1.1554900
## [334,] 0.9472861 1.2740448
## [335,] 1.1505387 1.6716664
## [336,] 0.8253657 0.8180606
## [337,] 1.3806115 0.9233959
## [338,] 1.1316007 1.1505848
## [339,] 1.2269568 0.7744473
## [340,] 0.9583078 0.9305569
## [341,] 0.9334629 1.0226110
## [342,] 0.9235831 0.9804556
## [343,] 1.1905077 0.9589890
## [344,] 0.8842472 1.0359307
## [345,] 0.9439784 1.1544557
## [346,] 1.0879927 1.0730570
## [347,] 1.1449022 0.7332432
## [348,] 1.1048502 1.2901438
## [349,] 0.8727622 0.9746256
## [350,] 0.9831825 0.8135057
## [351,] 0.7459438 1.0218761
## [352,] 0.9725103 0.9206295
## [353,] 1.3969209 0.9711060
## [354,] 0.8895518 0.7199623
## [355,] 0.8321088 0.7938412
## [356,] 1.0015908 0.9223267
## [357,] 1.1088094 1.1645725
## [358,] 0.9647698 1.1910636
## [359,] 0.9151823 1.0519044
## [360,] 0.8467949 0.8525618
## [361,] 0.9375881 0.8286475
## [362,] 1.0759430 1.1854977
## [363,] 0.8354431 0.8825938
## [364,] 1.2203555 1.5745379
## [365,] 0.9077643 0.9747892
## [366,] 1.1558771 1.0863934
## [367,] 0.8459257 0.8199661
## [368,] 0.9240758 1.1504240
## [369,] 1.3441147 1.1703953
## [370,] 1.0360751 1.0135145
## [371,] 0.9661962 0.9259997
## [372,] 0.8903508 1.0211540
## [373,] 1.0020499 0.9961410
## [374,] 0.8334780 1.0385235
## [375,] 0.8786680 0.9685488
## [376,] 0.9840099 0.9017707
## [377,] 0.8194386 0.7571217
## [378,] 1.2860489 0.9153718
## [379,] 1.0702213 1.1294028
## [380,] 1.2347931 1.1177022
## [381,] 0.8892962 0.7724243
## [382,] 1.1522986 1.2488344
## [383,] 0.9068692 0.9296854
## [384,] 0.8018364 0.5475286
## [385,] 1.0570498 0.9362962
## [386,] 1.2248845 0.8679932
## [387,] 0.8858413 0.8012275
## [388,] 1.1374236 0.9327977
## [389,] 0.8964508 0.8574513
## [390,] 0.9137466 1.1509275
## [391,] 1.0235198 0.9268868
## [392,] 0.8324792 0.7728359
## [393,] 1.1582068 1.0911831
## [394,] 0.9643542 1.2392439
## [395,] 1.0562611 0.9925274
## [396,] 1.1188405 1.6045315
## [397,] 1.0498572 1.2648459
## [398,] 0.9792722 1.0620945
## [399,] 0.8725656 0.9095487
## [400,] 0.7970539 0.8095415
## [401,] 1.0747833 1.3946270
## [402,] 0.7297564 0.7304721
## [403,] 1.2290197 1.1157534
## [404,] 0.8766704 0.8659397
## [405,] 1.0801498 1.0414327
## [406,] 0.8372594 1.1277636
## [407,] 1.1460712 0.8586834
## [408,] 0.8495929 1.0981643
## [409,] 0.9754059 1.1521310
## [410,] 0.9765771 1.1037708
## [411,] 0.8197834 0.9915042
## [412,] 1.1691479 1.1226246
## [413,] 0.9369109 1.1337906
## [414,] 0.8597371 0.8676621
## [415,] 0.9705766 0.9612007
## [416,] 1.4053453 1.0180953
## [417,] 1.2523909 0.7751819
## [418,] 0.8506695 0.9741106
## [419,] 0.8894310 0.7643555
## [420,] 0.8014763 0.8986224
## [421,] 1.1836242 1.4119073
## [422,] 0.9752538 1.1024748
## [423,] 0.6801935 0.7539376
## [424,] 0.9424596 0.7227529
## [425,] 0.9255313 0.9668788
## [426,] 0.9645816 1.0735946
## [427,] 0.8999194 1.1169306
## [428,] 0.8895277 0.9990581
## [429,] 1.0799638 1.2663881
## [430,] 0.8903421 1.2227805
## [431,] 1.1838503 0.8624211
## [432,] 1.0241618 0.7148077
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## [442,] 1.0807169 1.0178577
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## [444,] 1.3926232 1.1605922
## [445,] 0.8285270 0.6731463
## [446,] 1.4418486 1.0835018
## [447,] 0.8823850 1.0599423
## [448,] 1.0397504 1.4165172
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## [450,] 0.8282402 0.7610899
## [451,] 1.2029547 0.9066483
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## [454,] 1.0457651 0.9897145
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## [457,] 1.4130877 0.8191948
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## [460,] 1.4252054 1.1272527
## [461,] 1.1157218 0.9309311
## [462,] 0.8714748 1.2351497
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## [465,] 0.7950897 0.9562251
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## [467,] 0.7955654 0.7950480
## [468,] 1.1724451 1.2201076
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## [470,] 1.0500869 0.9050799
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## [477,] 1.3313780 0.9964611
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## [481,] 0.9040359 1.2003595
## [482,] 1.0885465 1.0843326
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## [485,] 1.1745150 0.9997497
## [486,] 0.7671113 0.6062066
## [487,] 0.8304969 0.7641393
## [488,] 0.9068432 0.8484416
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## [490,] 0.8988281 0.8410159
## [491,] 1.1441597 0.9568104
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## [500,] 1.0086437 1.0927511
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## [503,] 0.9581001 1.3414987
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## [509,] 1.1178864 1.5858061
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## [511,] 0.8980365 0.9656792
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## [513,] 1.0272688 1.1278770
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## [515,] 1.1121755 0.7010774
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## [518,] 0.8313080 0.6689749
## [519,] 0.8463715 0.9025559
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## [527,] 0.9397029 0.8332270
## [528,] 1.1542833 1.0940622
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## [530,] 1.5175200 1.2190898
## [531,] 1.3475730 0.9867358
## [532,] 1.2311168 0.8686386
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## [534,] 1.1159308 0.9117076
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## [549,] 1.0952677 1.2494209
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## [551,] 0.9911316 0.7054064
## [552,] 1.4967367 0.9831982
## [553,] 1.2702762 0.8953689
## [554,] 1.3421103 0.9766552
## [555,] 1.1660790 1.0520501
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## [558,] 0.9237289 1.0111019
## [559,] 0.9083371 0.8910060
## [560,] 0.7148161 0.7474786
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## [562,] 0.9312675 0.9453406
## [563,] 1.1803352 0.9895299
## [564,] 0.9974212 0.9112288
## [565,] 0.9942754 1.1214666
## [566,] 1.0480474 0.7088059
## [567,] 0.6575838 0.7167451
## [568,] 0.9608140 1.0570694
## [569,] 1.0787269 0.8109374
## [570,] 1.3769576 1.1619868
## [571,] 0.9959614 1.0519974
## [572,] 0.8927101 0.8818347
## [573,] 0.7973760 0.8632731
## [574,] 0.8781703 1.1212683
## [575,] 1.0221562 0.9107141
## [576,] 1.5060247 0.9986078
## [577,] 1.2892990 1.1426447
## [578,] 0.9082846 0.8749597
## [579,] 1.2144048 1.1915012
## [580,] 1.1148657 1.4018671
## [581,] 1.1798356 1.0108688
## [582,] 0.8237682 0.9145178
## [583,] 1.0421321 1.1284264
## [584,] 1.0656758 0.6921696
## [585,] 1.0658346 1.0136370
## [586,] 0.8843183 0.8696266
## [587,] 0.9642890 0.9933635
## [588,] 1.1279147 1.3704610
## [589,] 1.0820833 1.2484599
## [590,] 0.8589963 0.8104763
## [591,] 1.0240141 0.7267184
## [592,] 0.9143472 0.9552111
## [593,] 0.8828614 0.7934863
## [594,] 1.2907187 0.9608899
## [595,] 1.3709075 1.0061389
## [596,] 1.0243512 1.1497412
## [597,] 1.4971976 0.9911098
## [598,] 1.1450677 1.0390080
## [599,] 1.0300643 1.1271092
## [600,] 1.0684456 1.5833211
## [601,] 0.9595367 0.9759528
## [602,] 1.1504045 0.9098073
## [603,] 1.1490591 0.7214711
## [604,] 0.8074763 0.8319270
## [605,] 1.0126300 0.8465653
## [606,] 1.1387590 0.9395431
## [607,] 0.9903471 1.0677796
## [608,] 0.7378982 0.6886856
## [609,] 0.9662773 0.8740253
## [610,] 0.9608148 0.9899144
## [611,] 1.2272885 1.3440858
## [612,] 0.8763618 1.0896068
## [613,] 1.1931403 1.2683514
## [614,] 0.9250789 1.2079512
## [615,] 0.7669429 0.7794204
## [616,] 0.9171175 1.0160921
## [617,] 0.7433968 0.7596221
## [618,] 0.8229206 1.0630548
## [619,] 0.8364976 1.0050405
## [620,] 0.9595456 1.2710908
## [621,] 0.9934958 0.7687502
## [622,] 0.7994724 1.0247581
## [623,] 0.7902883 1.0516512
## [624,] 0.8466349 1.1210834
## [625,] 1.0182304 1.0038691
## [626,] 0.8258651 0.7242357
## [627,] 1.4015697 1.1429499
## [628,] 1.3178685 1.2938181
## [629,] 1.0632873 0.9277636
## [630,] 0.8271498 0.7739249
## [631,] 0.8553083 0.9428036
## [632,] 1.0599927 1.1230428
## [633,] 1.3059745 1.2864307
## [634,] 1.2074015 1.1244135
## [635,] 0.8051013 1.0465159
## [636,] 0.8063736 0.9069611
## [637,] 0.9818285 1.0800002
## [638,] 0.9452951 1.1032836
## [639,] 0.9406708 1.0071179
## [640,] 1.3284354 0.9656821
## [641,] 0.7346117 0.6603188
## [642,] 1.2441205 1.1942798
## [643,] 1.2009845 0.7892181
## [644,] 0.8646547 1.0223850
## [645,] 1.3073631 1.5722734
## [646,] 1.0250009 0.9031840
## [647,] 0.9536624 1.1783853
## [648,] 1.1862750 1.0420095
## [649,] 0.7575625 0.8136649
## [650,] 0.9029284 0.9866084
## [651,] 0.8457157 0.9423593
## [652,] 1.1863245 1.4661013
## [653,] 0.9624093 1.1237651
## [654,] 0.9957217 1.0949456
## [655,] 1.0482231 0.8476525
## [656,] 0.8741259 0.8524817
## [657,] 0.9555552 0.9290137
## [658,] 0.7568346 0.6981095
## [659,] 0.8882204 0.9400896
## [660,] 1.3206111 0.8180770
## [661,] 1.2031149 1.2360162
## [662,] 0.8555527 1.0054115
## [663,] 1.2195857 1.1349313
## [664,] 0.8199159 0.7748973
## [665,] 0.9671718 1.4218091
## [666,] 0.9312522 0.8653532
## [667,] 0.7151748 0.7923122
## [668,] 1.6535752 1.1589891
## [669,] 1.1760195 1.2364990
## [670,] 1.0623395 1.0122073
## [671,] 1.4994530 1.0473084
## [672,] 1.4706016 1.0882971
## [673,] 1.0745438 1.3377947
## [674,] 1.1317734 1.3334728
## [675,] 0.9471077 0.9817540
## [676,] 0.8100260 0.6953008
## [677,] 0.8234994 0.8157302
## [678,] 0.9670493 0.9011264
## [679,] 1.1259783 0.9966803
## [680,] 0.8008615 0.8636226
## [681,] 1.0823943 1.2344912
## [682,] 0.9090052 0.7729449
## [683,] 0.8798376 1.1528359
## [684,] 0.8189700 0.7906862
## [685,] 0.7827280 0.7279962
## [686,] 0.8832458 1.0253818
## [687,] 0.7887463 0.8781827
## [688,] 0.9867923 0.8472245
## [689,] 1.0634514 1.1771721
## [690,] 0.9479929 1.3316592
## [691,] 1.6384480 1.1346643
## [692,] 1.1937805 1.4244644
## [693,] 0.8578407 1.1258747
## [694,] 0.8395580 0.7753374
## [695,] 0.8579800 0.8515674
## [696,] 0.8929692 0.9049618
## [697,] 1.2335510 0.9361922
## [698,] 1.4664905 0.9660881
## [699,] 0.9031006 1.0869686
## [700,] 0.8478174 0.8809320
## [701,] 0.8697306 0.7729489
## [702,] 0.8115761 0.8421545
## [703,] 1.2015233 1.4340160
## [704,] 1.0624816 0.7826133
## [705,] 1.0912161 0.8508818
## [706,] 0.9646415 1.0198309
## [707,] 1.0116670 0.8754954
## [708,] 0.9939614 1.2373074
## [709,] 0.9618637 0.9487582
## [710,] 1.0138454 1.0037753
## [711,] 1.1124341 1.3124262
## [712,] 0.8405409 0.8564074
## [713,] 0.8765413 0.8060267
## [714,] 1.0313662 0.8644737
## [715,] 1.1294820 1.1625088
## [716,] 0.8212777 0.7053702
## [717,] 0.7425487 0.7883213
## [718,] 0.8823233 0.8481776
## [719,] 1.2184049 1.4305914
## [720,] 1.1090313 1.2839713
## [721,] 1.0794271 1.3412266
## [722,] 1.3803572 0.9229496
## [723,] 1.1307897 1.2214085
## [724,] 0.6655254 0.6481444
## [725,] 1.2223288 0.9284394
## [726,] 1.0688501 1.0719749
## [727,] 1.3030634 0.9155104
## [728,] 0.9573354 1.1404477
## [729,] 1.1004579 0.8973787
## [730,] 0.9295955 0.9600667
## [731,] 0.9992659 0.9005569
## [732,] 1.6378827 1.3531655
## [733,] 0.8201965 1.1130748
## [734,] 1.1873336 1.2395266
## [735,] 0.9774942 0.8531910
## [736,] 0.8796710 0.8739049
## [737,] 1.4744257 1.1661782
## [738,] 0.9994928 1.1174150
## [739,] 0.9355978 0.9449902
## [740,] 1.0949576 1.2874203
## [741,] 1.3768718 1.1463446
## [742,] 0.8576736 0.7786925
## [743,] 1.2168519 1.1509463
## [744,] 1.2310825 1.2653594
## [745,] 0.9554596 1.1100078
## [746,] 0.9785907 1.1277205
## [747,] 0.6965759 0.7483674
## [748,] 0.7239748 0.7747933
## [749,] 0.9418582 0.9458627
## [750,] 1.4252516 1.0772577
## [751,] 1.0680822 1.1749805
## [752,] 0.8653809 1.2208553
## [753,] 1.0820179 1.1544577
## [754,] 1.0465571 1.1473167
## [755,] 0.8850585 1.1140585
## [756,] 1.0342309 1.1431133
## [757,] 1.0000553 1.0421328
## [758,] 0.9646298 0.7470418
## [759,] 0.9653770 0.8915016
## [760,] 1.0377525 1.1104371
## [761,] 0.8016455 0.6750751
## [762,] 0.8674828 0.9319376
## [763,] 0.8612449 0.8308213
## [764,] 1.0888787 1.0655153
## [765,] 0.8800079 1.1003809
## [766,] 1.2497405 1.4192221
## [767,] 0.7840666 0.8992395
## [768,] 0.8462230 0.9803345
## [769,] 1.0248362 1.0327393
## [770,] 0.7715156 0.6624294
## [771,] 1.0144749 0.9700797
## [772,] 0.9565803 0.9213343
## [773,] 0.9428110 1.0316421
## [774,] 1.1203155 1.1538110
## [775,] 0.8923684 0.6572675
## [776,] 1.1979562 1.2437542
## [777,] 1.1583102 1.4077319
## [778,] 0.9787929 0.6809072
## [779,] 1.2154446 1.5336866
## [780,] 1.1812711 1.2798867
## [781,] 1.1213434 0.9964038
## [782,] 0.9393269 1.0988165
## [783,] 1.2261197 1.0083325
## [784,] 0.8017994 0.8319754
## [785,] 0.9176935 1.2899664
## [786,] 0.8268666 0.7842430
## [787,] 0.7652806 0.8697081
## [788,] 0.7415575 0.8382894
## [789,] 0.9695706 0.8582070
## [790,] 1.0979369 1.5765650
## [791,] 0.7600740 0.7703445
## [792,] 1.2049876 1.2625119
## [793,] 0.8237252 0.8027471
## [794,] 1.5406221 1.1074253
## [795,] 1.1292038 0.8228513
## [796,] 1.2186348 1.1531901
## [797,] 0.9380929 0.9515107
## [798,] 1.0383300 1.2591996
## [799,] 1.2111358 1.1855037
## [800,] 0.8672311 1.0357544
## [801,] 0.9005554 0.9644369
## [802,] 0.9341105 1.1480250
## [803,] 1.3725052 1.0294293
## [804,] 0.8982613 1.0182232
## [805,] 1.1144322 1.2719436
## [806,] 1.0641924 1.1108379
## [807,] 0.8907793 1.0811199
## [808,] 1.4490413 1.1202655
## [809,] 1.1118995 1.1223700
## [810,] 0.9829853 0.8046756
## [811,] 1.0204120 0.8182327
## [812,] 0.8206759 0.9749901
## [813,] 1.0239948 1.0005067
## [814,] 0.8413453 0.9895975
## [815,] 1.0432933 1.2018050
## [816,] 1.1300556 0.7716239
## [817,] 0.8067842 0.8274120
## [818,] 0.8878230 1.0849221
## [819,] 1.1234790 1.1207520
## [820,] 1.0190822 0.9873752
## [821,] 0.9517965 1.1219984
## [822,] 0.9022431 0.8749648
## [823,] 0.6092081 0.6648664
## [824,] 0.8504473 0.8094771
## [825,] 1.0586349 1.2074618
## [826,] 0.9014688 0.9970189
## [827,] 0.8290326 0.7731403
## [828,] 1.0204077 0.9013266
## [829,] 0.9150651 0.9434033
## [830,] 0.9872483 1.0018633
## [831,] 0.8808159 1.0238260
## [832,] 1.0423452 1.0085540
## [833,] 1.3995883 1.1737465
## [834,] 0.8766723 1.0285116
## [835,] 1.1475700 1.4491169
## [836,] 0.9608148 0.9899144
## [837,] 0.9434018 1.0407879
## [838,] 1.2474747 1.0860268
## [839,] 0.9517166 1.2540850
## [840,] 0.9178702 0.8726524
## [841,] 0.9075137 0.8069502
## [842,] 0.9174268 0.9715202
## [843,] 0.7627844 0.8185525
## [844,] 0.9698816 1.1750516
## [845,] 0.8896662 1.1325824
## [846,] 0.7940434 0.6109937
## [847,] 0.7131411 0.7578657
## [848,] 0.9795560 1.0390760
## [849,] 0.9083493 0.8643287
## [850,] 1.0759322 1.0105926
## [851,] 1.0681861 1.0790214
## [852,] 1.0614957 1.3971435
## [853,] 1.0017547 0.8471292
## [854,] 0.8154865 0.9908245
## [855,] 1.0799074 1.4168149
## [856,] 1.1743886 0.7571724
## [857,] 1.0923075 0.9860912
## [858,] 0.8298103 0.6463813
## [859,] 1.0861248 1.2981766
## [860,] 1.1054961 1.0191428
## [861,] 1.2489605 0.7689500
## [862,] 1.0294195 0.8521888
## [863,] 0.9563037 1.2423652
## [864,] 0.9872489 1.1295691
## [865,] 0.8677390 1.2746663
## [866,] 1.2366070 1.0362699
## [867,] 1.0179758 1.1332495
## [868,] 0.8699113 0.8036200
## [869,] 1.0023747 1.0928934
## [870,] 1.1787194 1.2878420
## [871,] 0.9009150 0.7148741
## [872,] 0.9662284 1.2119608
## [873,] 0.7576574 0.5956411
## [874,] 0.9451158 1.0164001
## [875,] 0.8484433 0.9475326
## [876,] 1.1159475 0.9865096
## [877,] 1.1477535 1.1072671
## [878,] 0.9797820 0.9334428
## [879,] 1.0596272 1.2451275
## [880,] 1.0006308 0.9949544
## [881,] 1.0750511 1.1383153
## [882,] 1.0821003 0.8697494
## [883,] 1.0827986 0.8043594
## [884,] 1.0861175 1.0405458
## [885,] 1.2789809 1.1695270
## [886,] 1.0350200 0.9007766
## [887,] 0.8519192 0.7626937
## [888,] 0.9048148 0.9550008
## [889,] 1.0023264 0.9589547
## [890,] 1.1780625 0.9765703
## [891,] 1.0902553 1.2949207
## [892,] 0.7657973 0.7874400
## [893,] 1.3294215 1.0340341
## [894,] 1.0260881 1.1578823
## [895,] 0.7895384 0.9478399
## [896,] 0.9407229 0.7660504
## [897,] 0.7549322 0.6540444
## [898,] 0.9536874 0.7174174
## [899,] 0.8818741 0.7075909
## [900,] 1.5574506 1.2411536
## [901,] 1.0658620 0.9752208
## [902,] 0.8636265 0.9788958
## [903,] 1.0585484 1.3743246
## [904,] 0.6812832 0.6515131
## [905,] 0.8702257 0.7451173
## [906,] 1.0754237 1.1888221
## [907,] 1.0366852 1.0013293
## [908,] 0.9995342 0.8621306
## [909,] 1.0971440 0.8626939
## [910,] 1.4232627 0.9838266
## [911,] 1.5899531 0.9865231
## [912,] 1.5098554 1.4818775
## [913,] 0.7706932 0.6550986
## [914,] 0.8564592 0.8536594
## [915,] 1.3106809 0.9983124
## [916,] 1.0465379 0.7568226
## [917,] 1.1423161 1.0975263
## [918,] 1.2599220 0.7849282
## [919,] 0.7856900 0.9630662
## [920,] 0.8241907 0.9788052
## [921,] 1.0287310 0.8127956
## [922,] 0.8179429 1.0435796
## [923,] 0.8371532 1.1011680
## [924,] 1.0635863 0.9632901
## [925,] 0.9306157 0.9059904
## [926,] 1.0032030 0.9484892
## [927,] 1.1014398 0.8593719
## [928,] 0.8005289 0.7468838
## [929,] 1.0466191 0.7786806
## [930,] 0.8510820 0.8473879
## [931,] 1.2118481 1.1243905
## [932,] 0.8639700 1.1023001
## [933,] 1.1017669 1.2180802
## [934,] 1.1055281 1.4552279
## [935,] 0.8468172 1.0313703
## [936,] 0.9902147 0.8304899
## [937,] 1.0442251 0.7630756
## [938,] 1.0521504 0.8903210
## [939,] 0.9750702 1.0023183
## [940,] 0.8545725 1.0310564
## [941,] 0.9624693 1.3682759
## [942,] 1.2367676 0.9562908
## [943,] 1.1505387 1.6716664
## [944,] 1.1551780 0.8774259
## [945,] 1.0482274 1.1580854
## [946,] 1.0342309 1.1431133
## [947,] 0.8670308 1.1085177
## [948,] 1.0743067 1.3848268
## [949,] 1.0284775 0.8335853
## [950,] 0.7882144 0.8225803
## [951,] 0.8871574 0.8002286
## [952,] 0.9149705 0.9762213
## [953,] 0.8613754 0.9548972
## [954,] 1.0135309 1.0165374
## [955,] 0.9978359 0.8521817
## [956,] 1.0573904 0.9046225
## [957,] 1.3279451 1.3664765
## [958,] 0.8661492 1.0611010
## [959,] 1.1284609 0.9583839
## [960,] 1.4883526 1.3065972
## [961,] 1.0274615 1.0265689
## [962,] 0.7720150 0.8867931
## [963,] 1.1503312 1.0839691
## [964,] 1.4245235 1.8323839
## [965,] 1.0411764 1.0005472
## [966,] 1.6378827 1.3531655
## [967,] 1.1091143 1.0959738
## [968,] 1.3093758 1.2619024
## [969,] 0.9950083 1.2953977
## [970,] 1.0967705 0.9537356
## [971,] 0.8741003 0.7551137
## [972,] 1.2341962 1.2495527
## [973,] 0.7519871 0.8213421
## [974,] 0.9856428 1.4435310
## [975,] 1.0687806 0.9760687
## [976,] 0.7570545 1.0615182
## [977,] 1.1865500 1.4513231
## [978,] 1.0969054 1.3440649
## [979,] 1.0112667 0.9322210
## [980,] 1.1896496 1.3656749
## [981,] 1.0670192 1.3469078
## [982,] 1.0794982 0.7379870
## [983,] 0.8482538 0.8468465
## [984,] 1.3173230 0.9652958
## [985,] 1.6535752 1.1589891
## [986,] 1.0141425 0.8891776
## [987,] 0.9719172 0.9174639
## [988,] 1.0011654 0.6959260
## [989,] 0.7651313 0.8095276
## [990,] 1.0749398 1.3810250
## [991,] 1.2050766 1.2519167
## [992,] 1.6400472 1.0898700
## [993,] 1.2311581 0.8606657
## [994,] 1.2479200 0.9792822
## [995,] 0.8430941 0.9036120
## [996,] 1.1293111 1.6756912
## [997,] 0.7675634 0.7406246
## [998,] 0.9813924 1.1521366
## [999,] 1.0201591 1.2959360
##
## $model.matrix
## (Intercept) treatment1 treatment2 treatment3 mesocosm1 mesocosm2
## 1 1 -1 -1 -1 -1 -1
## 2 1 1 0 0 -1 -1
## 3 1 -1 -1 -1 1 0
## 4 1 0 1 0 0 1
## 5 1 0 1 0 -1 -1
## 6 1 -1 -1 -1 -1 -1
## 7 1 0 0 1 -1 -1
## 8 1 0 0 1 0 1
## 9 1 1 0 0 1 0
##
## $terms
## dist_tab_assay ~ treatment + mesocosm
## attr(,"variables")
## list(dist_tab_assay, treatment, mesocosm)
## attr(,"factors")
## treatment mesocosm
## dist_tab_assay 0 0
## treatment 1 0
## mesocosm 0 1
## attr(,"term.labels")
## [1] "treatment" "mesocosm"
## attr(,"order")
## [1] 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"
anova(betadisper(dist_tab_assay,samplesHedophylum$treatment))
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 3 1654.4 551.46 1.6828 0.2847
## Residuals 5 1638.5 327.70
anova(betadisper(dist_tab_assay,samplesHedophylum$mesocosm))
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 2115.1 1057.55 2.3219 0.1791
## Residuals 6 2732.8 455.47
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] EnhancedVolcano_1.12.0 ashr_2.2-54
## [3] apeglm_1.16.0 tximport_1.22.0
## [5] ggvenn_0.1.10 limma_3.50.3
## [7] dplyr_1.1.1 vegan_2.6-4
## [9] lattice_0.20-45 permute_0.9-7
## [11] gplots_3.1.3 genefilter_1.76.0
## [13] RColorBrewer_1.1-3 pheatmap_1.0.12
## [15] markdown_1.5 ggrepel_0.9.3
## [17] ggplot2_3.4.2 BiocManager_1.30.20
## [19] devtools_2.4.5 usethis_2.1.6
## [21] vsn_3.62.0 adegenet_2.1.10
## [23] ade4_1.7-22 DESeq2_1.34.0
## [25] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [27] MatrixGenerics_1.6.0 matrixStats_0.63.0
## [29] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
## [31] IRanges_2.28.0 S4Vectors_0.32.4
## [33] BiocGenerics_0.40.0
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.8 igraph_1.4.1 splines_4.1.2
## [4] BiocParallel_1.28.3 digest_0.6.31 invgamma_1.1
## [7] htmltools_0.5.5 SQUAREM_2021.1 fansi_1.0.4
## [10] magrittr_2.0.3 memoise_2.0.1 cluster_2.1.4
## [13] tzdb_0.3.0 remotes_2.4.2 readr_2.1.4
## [16] Biostrings_2.62.0 annotate_1.72.0 extrafont_0.19
## [19] vroom_1.6.1 extrafontdb_1.0 bdsmatrix_1.3-6
## [22] prettyunits_1.1.1 colorspace_2.1-0 blob_1.2.4
## [25] xfun_0.38 hexbin_1.28.3 callr_3.7.3
## [28] crayon_1.5.2 RCurl_1.98-1.10 jsonlite_1.8.4
## [31] survival_3.5-5 ape_5.7-1 glue_1.6.2
## [34] gtable_0.3.3 zlibbioc_1.40.0 XVector_0.34.0
## [37] seqinr_4.2-23 proj4_1.0-12 DelayedArray_0.20.0
## [40] pkgbuild_1.4.0 Rttf2pt1_1.3.12 maps_3.4.1
## [43] scales_1.2.1 mvtnorm_1.1-3 DBI_1.1.3
## [46] miniUI_0.1.1.1 Rcpp_1.0.10 xtable_1.8-4
## [49] emdbook_1.3.12 bit_4.0.5 preprocessCore_1.56.0
## [52] truncnorm_1.0-9 profvis_0.3.7 htmlwidgets_1.6.2
## [55] httr_1.4.5 ellipsis_0.3.2 farver_2.1.1
## [58] urlchecker_1.0.1 pkgconfig_2.0.3 XML_3.99-0.14
## [61] sass_0.4.5 locfit_1.5-9.7 utf8_1.2.3
## [64] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.0
## [67] reshape2_1.4.4 later_1.3.0 AnnotationDbi_1.56.2
## [70] munsell_0.5.0 tools_4.1.2 cachem_1.0.7
## [73] cli_3.6.1 generics_0.1.3 RSQLite_2.3.0
## [76] evaluate_0.20 stringr_1.5.0 fastmap_1.1.1
## [79] yaml_2.3.7 processx_3.8.0 knitr_1.42
## [82] bit64_4.0.5 fs_1.6.1 caTools_1.18.2
## [85] purrr_1.0.1 KEGGREST_1.34.0 nlme_3.1-162
## [88] mime_0.12 ash_1.0-15 ggrastr_1.0.1
## [91] compiler_4.1.2 rstudioapi_0.14 beeswarm_0.4.0
## [94] curl_5.0.0 png_0.1-8 affyio_1.64.0
## [97] tibble_3.2.1 geneplotter_1.72.0 bslib_0.4.2
## [100] stringi_1.7.12 ps_1.7.3 ggalt_0.4.0
## [103] Matrix_1.5-1 vctrs_0.6.1 pillar_1.9.0
## [106] lifecycle_1.0.3 jquerylib_0.1.4 irlba_2.3.5.1
## [109] bitops_1.0-7 httpuv_1.6.9 R6_2.5.1
## [112] affy_1.72.0 promises_1.2.0.1 KernSmooth_2.23-20
## [115] vipor_0.4.5 sessioninfo_1.2.2 MASS_7.3-58.3
## [118] gtools_3.9.4 pkgload_1.3.2 withr_2.5.0
## [121] GenomeInfoDbData_1.2.7 hms_1.1.3 mgcv_1.8-42
## [124] parallel_4.1.2 coda_0.19-4 rmarkdown_2.21
## [127] mixsqp_0.3-48 bbmle_1.0.25 numDeriv_2016.8-1.1
## [130] shiny_1.7.4 ggbeeswarm_0.7.1